code: def setup_method(self, method):
      sparkConf = (
          init_spark_conf()
          .setMaster("local[4]")
          .setAppName("test wide and deep")
      )
      self.sc = init_nncontext(sparkConf)
      self.sqlContext = SQLContext(self.sc)
      data_path = os.path.join(
          os.path.split(__file__)[0], "../../resources/recommender"
      )
      categorical_gender_udf = udf(
          lambda gender: categorical_from_vocab_list(
              gender, ["F", "M"], start=1
          )
      )
      bucket_udf = udf(
          lambda feature1, feature2: hash_bucket(
              str(feature1) + "_" + str(feature2), bucket_size=100
          )
      )
      self.data_in = (
          self.sqlContext.read.parquet(data_path)
          .withColumn(
              "gender",
              categorical_gender_udf(col("gender")).cast("int"),
          )
          .withColumn(
              "occupation-gender",
              bucket_udf(col("occupation"), col("gender")).cast("int"),
          )
      )
      self.column_info = ColumnFeatureInfo(
          wide_base_cols=["occupation", "gender"],
          wide_base_dims=[21, 3],
          wide_cross_cols=["occupation-gender"],
          wide_cross_dims=[100],
          indicator_cols=["occupation", "gender"],
          indicator_dims=[21, 3],
          embed_cols=["userId", "itemId"],
          embed_in_dims=[100, 100],
          embed_out_dims=[20, 20],
          continuous_cols=["age"],
      )
  
file_path: analytics-zoo/pyzoo/test/zoo/models/recommendation/test_wideanddeep.py
imports: import pytest
  from pyspark.sql.functions import col, udf
  from zoo.models.recommendation import *
  from zoo.models.recommendation.utils import *
  from zoo.common.nncontext import *
  from test.zoo.pipeline.utils.test_utils import ZooTestCase
label_desc: Use image node_id 1 for calling the TestWideAndDeep obj's underlying member method code with example usage: obj.setup_method(method) without return types